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Parameter identification in a Proton Exchange Membrane Fuel Cell (PEMFC) entails the application of optimization algorithms to ascertain the optimal unknown variables essential for crafting an accurate model that predicts fuel-cell performance. These parameters are typically not included in the manufacturer's datasheet and must be identified to ensure precise modeling and forecasting of fuel cell behavior. This paper introduces a recently developed hybrid algorithm (Aquila Optimizer Arithmetic Algorithm Optimization (AOAAO)) that enhances the AO and AAO algorithm's efficiency through a novel mutation strategy, aimed at determining seven unknown parameters of a PEMFC during the optimization process. These parameters function as decision variables, and the objective function aimed for minimization is the sum square error (SSE) between the predicted and actual measured cell voltages. AOAAO demonstrated superior performance across various metrics, achieving an SSE minimum in comparison to other compared algorithm. AOAAO's robustness was validated through extensive testing with six commercially available PEMFCs, including BCS 500 W-PEM, 500 W SR-12PEM, Nedstack PS6 PEM, H-12 PEM, HORIZON 500 W PEM, and a 250 W-stack, across twelve case studies derived from various operational conditions detailed in manufacturers' datasheets. For each datasheet, both Current–Voltage (I/V) and Power–Voltage (P/V) characteristics of the PEMFCs scenarios closely aligned with those observed in experimental data, affirming AOAAO's superior accuracy, robustness, and time efficiency for real-time fuel cell modeling. In terms of computational efficiency, AOAAO runtime is significantly faster than all compared algorithms, demonstrating an efficiency improvement of approximately 98%. [ABSTRACT FROM AUTHOR]

ABSTRACT Agrovoltaics, also known as Agri‐PV or AV, is an innovative approach that entails the shared utilization of land for both the production of agricultural commodities and energy generation. This concept has gained immense popularity in recent times owing to its ability to boost income per unit of land area significantly. The scope of AV systems is quite extensive, as it encompasses solar energy converters and other renewable energy sources like bioenergy. Current strategies for agrovoltaic (AV) in agriculture are the outcome of the gradual development of agroecology and the integration of photovoltaic (PV) power supply into the grid. These approaches could lead to a nearly doubled income per unit area. Without on‐site power supply, reduced chemical fertilizers and pesticides, and on‐site yield processing, AV has the potential to revolutionize large‐scale unmanned precision agriculture and smart farming. These approaches might lead to significant changes in the logistics and value‐added production chain, thereby reducing agriculture's carbon footprint. In the future, it is possible to reduce the cost of AV technology by half by utilizing decommissioned solar panels in the technology and to delay the need for bulk PV recycling by several years. This review presents a different perspective to the common discourse on the topic, by giving special emphasis to the potential to further integrate AV into agriculture, which has the potential to facilitate the resolution of relevant legal disputes over the use of AV. [ABSTRACT FROM AUTHOR]

Two two-element slotted patch multiple-input multiple-output (MIMO) antenna with coplanar waveguide (CPW) feed is proposed for deployment in implantable medical devices. Implantable devices are compact and demand high-gain antennae with unidirectional radiation patterns. Regarding compactness, the antenna has a size of 16 × 6×0.25 mm3 for the operating frequency of 2.45 GHz ISM band. Miniaturized antenna geometry is attained using the slots in the radiator and with the coplanar waveguide feed. An element MIMO configuration is used for establishing a reliable communication link. The two radiators maintain a connected ground topology with a minimum edge-to-edge spacing of 7.7 mm. Mutual coupling of -29 dB and operating frequency spectrum of 1300 MHz is attained at the center cut off frequency. Unidirectional power pattern and high gain of 2.3 dB is achieved. The proposed MIMO structure is suitable for mitigating multipath fading effect and realizing higher spectral efficiency in short range communication. The in-tissue measurement results for the scattering parameters and radiation characteristics are done by placing the antenna in a pork loin. Specific absorption rate (SAR) analysis is performed to confirm the user's safety from nearfield EM waves. Antenna has safe SAR value of 0.798 W/kg for 1-g of average tissue for the power of 1-mW. The low envelope correlation coefficient of 0.035 depicts the excellent diversity performance for MIMO operation. [ABSTRACT FROM AUTHOR]

Fuel cell technology emerges as a promising green solution, offering mitigation to global warming, air pollution, and energy crises. This eco-friendly approach is witnessing a surge in adoption within the automotive sector, with fuel cell buses, cars, scooters, forklifts, and more, becoming increasingly prevalent. The automobile industry has been rapidly advancing fuel cell technology, inching closer to the commercialization of fuel cell vehicles. As various technical hurdles are surmounted and costs are reduced, fuel cell vehicles are poised to become a competitive force in the automobile market, presenting an excellent solution for environmental sustainability and energy efficiency. This review paper delves into the fundamentals of fuel cells, their characteristics, and their applications in the automotive realm, exploring their prospects in comparison to traditional technologies. Furthermore, it sheds light on the existing research and industrial developments in hydrogen and fuel cell technologies. Additionally, a comprehensive comparison is provided between various fuel cell cars that have already been commercialized, enabling readers to understand the current market landscape. The review also analyses the advantages and challenges associated with fuel cell technology, offering insights into its future development trajectory. Through this comprehensive exploration, readers can gain a deeper understanding of fuel cell technology and its potential in revolutionizing the automotive industry. [ABSTRACT FROM AUTHOR]

Parameter prediction for PV solar cells plays a crucial role in controlling and optimizing the performance of PV modules. In this study, the parameter prediction of a four‐diode PV model was carried out using the Improved Grey Wolf Optimization (IGWO) algorithm, which builds upon the Grey Wolf Optimization (GWO) algorithm. The parameters required for the four‐diode PV model were optimized based on a predefined objective function. Subsequently, the obtained data were compared with the data from RTCFrance Solar Cell to validate the accuracy and reliability of the optimization results. The evaluation of the optimization results revealed that the Sum Square Error (SSE) values for PSOGWO, AGWOCS, GWOCS, and GWO were 3.96E‐05, while the MSE value for IGWO was 3.6309E‐05. These findings clearly demonstrate that the proposed IGWO algorithm outperforms the other algorithms used in the study, based on the minimized SSE values. This study emphasizes the importance of parameter prediction in optimizing PV performance, and it contributes to thefield by introducing the novel IGWO algorithm for the four‐diode PV model. The algorithm's superior performance, as demonstrated through extensive testing and comparison with existing algorithms, validates its efficacy in accurately predicting the parameters for the PV solar cell model. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate modeling of photovoltaic (PV) modules/cells is crucial for evaluating the efficiency of solar PV systems. However, the lack of specific parameters of solar cells, which are not included in the manufacturer's datasheet, often results in flawed cell modeling. Quick and convenient parameter extraction techniques are required to overcome this challenge and create a robust solar PV cell model. These models are useful in optimization, simulation, and enhanced energy harvesting from PV-based renewable energy systems. This paper uses a recent optimizer called Rat Search Algorithm Particle Swarm Optimization (RSAPSO), for extracting the parameters of single diode and double diode models of PV cells. RSAPSO combines the exploration and exploitation advantages of both algorithms. The RSAPSO's parameter optimization results were compared against five other techniques, and the superiority of the suggested algorithm was confirmed through ranking tests, statistical error analyses, and temperature variation sensitivity analyses. [ABSTRACT FROM AUTHOR]